Data and Functions
Packages and Functions
library(Rcpp)
library(ggplot2)
library(plotly)
library(data.table)
library(ggpubr)
library(vegan)
library(phyloseq)
# devtools::install_github("schuyler-smith/schuylR")
library(schuylR)
# devtools::install_github("schuyler-smith/ssBLAST")
library(ssBLAST)
setwd('~/google-drive/DARTE/')
get_sample_names <- function(x){gsub('\\.','_',gsub('.blast.*','',colnames(x)))}Data Files
metadata <- fread('files/DARTE-QM-v3-metadata-fromKathyMou.csv')
metadata[[1]] <- gsub('\\.', '_', metadata[[1]])
categories <- fread('files/category_all.tsv', header = FALSE)
colnames(categories) <- c('Source', 'ARG_Class', 'Primer')
sample_metrics <- read.csv('darte-samples_R.csv')
sample_metrics <- sample_metrics[!(sample_metrics$Sample %in% c('Mock-0-025spike-C_S12', 'Mock-0spike-A_S1')),]Sequencing Results
Percent Identity
This is created by averaging the number of reads from all samples from a single source, i.e. the average number of reads aligned from soil at each percent identity threshold.
Relative Abundance
This is the same information as the Percent Identity graph, but represented as the proportion of the total number of reads from the raw FASTQ files.
Percent Aligned
This is the same information as the Percent Identity graph, but represented as the proportion of the total number of aligned reads. I.E. at each increasing percent identity, the number of reads aligned is divided by the number of reads aligned at 60% (the total number of aligned reads).
98% Identity match for reads of length >= 100 bp seems to be the ideal threshold for number of reads captured compared to confidence in alignment. For subsequent analyses, I used that threshold for determining identified ARGs.
Primer Success
Mock Samples
The rate of success for the primers can be determined by how many successfully amplified their intended target.
length(expected_mock)## [1] 98primer_success## [1] 73round(primer_success/length(expected_mock) * 100, 1)## [1] 74.5Of the 98 genes expected to be found in the Mock communities, 73 were captured by the primers.
Overall Success
This metric for success, is not necessarily fair; the primers were not made with the certainty that every target was present within any of the samples. The Mock communities were designed with known members, and thereby a better idea of the expected targets that would be amplified.
length(primers)## [1] 762primer_success## [1] 203round(primer_success/length(primers) * 100, 1)## [1] 26.6
Unexpected
I addition to the 73 genes that were expected, there were an additional 61 genes detected that we did not predict.
unexpected## [1] 63Unexpected_graph
I addition to the 73 genes that were expected, there were an additional 61 genes detected that we did not predict. The notable is the ?sulfanomides? that we did not expect to be there, as well as the aph-6 and the lnu_C. The other odditty is how much more abundant these are than the expected. Max value of 15000, compared to ~2500.
Of the 97 genes expected to be found in the Mock communities, 72 were captured by the primers.
Spike Detection
Bar Graphs
Linear Relation
Table
## Sample Spike Spike_Level Color
## 1: Q3_Mock_0spike_B 0 0.0000 #757575
## 2: Q3_Mock_0spike_C 1 0.0000 #757575
## 3: Q3_Mock_0_0025spike_A 20 0.0025 #E69F00
## 4: Q3_Mock_0_0025spike_B 11 0.0025 #E69F00
## 5: Q3_Mock_0_0025spike_C 12 0.0025 #E69F00
## 6: Q3_Mock_0_009spike_A 37 0.0090 #56B4E9
## 7: Q3_Mock_0_009spike_B 70 0.0090 #56B4E9
## 8: Q3_Mock_0_009spike_C 87 0.0090 #56B4E9
## 9: Q3_Mock_0_025spike_A 223 0.0250 #009E73
## 10: Q3_Mock_0_025spike_B 159 0.0250 #009E73
## 11: Q3_Mock_0_12spike_A 634 0.1200 #0072B2
## 12: Q3_Mock_0_12spike_B 562 0.1200 #0072B2
## 13: Q3_Mock_0_12spike_C 972 0.1200 #0072B2
## 14: Q3_Mock_0_25spike_A 2027 0.2500 #D55E00
## 15: Q3_Mock_0_25spike_B 1675 0.2500 #D55E00
## 16: Q3_Mock_0_25spike_C 1053 0.2500 #D55E00Nomal. Bar Graphs
Nomal. Linear Relation
Table
## Sample Spike Aligned Count Spike_Level Color
## 1: Q3_Mock_0spike_B 0 23795 0.00 0.0000 #757575
## 2: Q3_Mock_0spike_C 1 42946 0.00 0.0000 #757575
## 3: Q3_Mock_0_0025spike_A 20 55061 0.04 0.0025 #E69F00
## 4: Q3_Mock_0_0025spike_B 11 24207 0.05 0.0025 #E69F00
## 5: Q3_Mock_0_0025spike_C 12 15981 0.08 0.0025 #E69F00
## 6: Q3_Mock_0_009spike_A 37 29794 0.12 0.0090 #56B4E9
## 7: Q3_Mock_0_009spike_B 70 44313 0.16 0.0090 #56B4E9
## 8: Q3_Mock_0_009spike_C 87 45024 0.19 0.0090 #56B4E9
## 9: Q3_Mock_0_025spike_A 223 37222 0.60 0.0250 #009E73
## 10: Q3_Mock_0_025spike_B 159 27588 0.58 0.0250 #009E73
## 11: Q3_Mock_0_12spike_A 634 56530 1.12 0.1200 #0072B2
## 12: Q3_Mock_0_12spike_B 562 71342 0.79 0.1200 #0072B2
## 13: Q3_Mock_0_12spike_C 972 70902 1.37 0.1200 #0072B2
## 14: Q3_Mock_0_25spike_A 2027 85052 2.38 0.2500 #D55E00
## 15: Q3_Mock_0_25spike_B 1675 54115 3.10 0.2500 #D55E00
## 16: Q3_Mock_0_25spike_C 1053 27182 3.87 0.2500 #D55E00Mock Samples - ARG Amplicon Detection
Each primer/ARG genes presented is expected to be found based on the mock.expected.fa file. 75% of the primers expected to bind genes in the mock samples were successful, and detected in the sequencing. 87% of the ARG genes expected to be found in the mock samples were found.
ARGs in Mock
BLAST against mock.expected.fa, compare to mock.expected.fa. Looking to see how many of the known ARGs are detected. Something is wrong with Q3_Mock_0_025spike_C.
By Primer
Same data, but by individual primer pressence.
Sample Ordination
Non-Normalized PCA
Distances were calculated from raw read counts to compare to how normalizing with relative abundance clusters replicates. Without housekeeping genes, we can’t directly compare samples to each other in terms of abundance or expression of certain genes. With relative abundance though, we should be able to compare the ratios.
Normalized PCA
Using relative abundance to normalize samples clusters the replicates tighter, indicating that it works well overall as a normalization method.
NMDS
Hierarchical Clustering
Bray-Curtis
Jaccard
Euclidean
Gower
K-Means Clustering
Optimize K
K(4)
K(6)
K(15)
Sample Compositon
ARG Profile
NGS vs PCR
